Small cell identification using machine learning

ABSTRACT

Small cell identification using machine learning is provided. A method can include extracting, by a device comprising a processor, signal strength information for a cell in a cellular communication network from user equipment log data; estimating, by the device, path loss information associated with the cell at respective distances based on the signal strength information for the cell, resulting in estimated path loss information; and, based on the estimated path loss information, optionally along with other information such as antenna transmission power, antenna transmission frequency band, percentage of user equipments having a distance to a base station within a threshold, maximum user equipment distance to a base station, etc., classifying, by the device, the cell as a type from a group of types of cells, the group comprising a macro cell and a small cell.

TECHNICAL FIELD

The present disclosure relates to cellular communication systems, and,in particular, to techniques for management and development of acellular communication system.

DESCRIPTION OF DRAWINGS

FIG. 1 is a diagram of an example cellular communication environment inwhich various aspects described herein can function.

FIG. 2 is a block diagram of a system that facilitates small cellidentification using machine learning in accordance with various aspectsdescribed herein.

FIG. 3 is a block diagram of an example, non-limiting cellclassification system in accordance with various aspects describedherein.

FIGS. 4-5 depict example cellular signal data that can be utilized forcell classification in accordance with various aspects described herein.

FIG. 6 is a block diagram of a system that facilitates small cellidentification using a support vector machine in accordance with variousaspects described herein.

FIG. 7 depicts an example data structure that can be utilized by thesystem of FIG. 6 in accordance with various aspects described herein.

FIG. 8 is a block diagram of a system that facilitates small cellidentification using a neural network in accordance with various aspectsdescribed herein.

FIG. 9 depicts an example data structure that can be utilized by thesystem of FIG. 8 in accordance with various aspects described herein.

FIG. 10 is a block diagram of a system that facilitates interpolation ofcell path loss data in accordance with various aspects described herein.

FIG. 11 depicts example interpolations that can be performed by thesystem of FIG. 10 in accordance with various aspects described herein.

FIG. 12 is a flow diagram of a method for small cell classificationusing machine learning in accordance with various aspects describedherein.

FIG. 13 depicts an example computing environment in which variousembodiments described herein can function.

DETAILED DESCRIPTION

Various specific details of the disclosed embodiments are provided inthe description below. One skilled in the art will recognize, however,that the techniques described herein can in some cases be practicedwithout one or more of the specific details, or with other methods,components, materials, etc. In other instances, well-known structures,materials, or operations are not shown or described in detail to avoidobscuring certain aspects.

In an aspect, a method as described herein can include extracting, by adevice including a processor, signal strength information for a cell ina cellular communication network from user equipment log data. Themethod can further include estimating, by the device, path lossinformation associated with the cell at respective distances based onthe signal strength information for the cell, resulting in estimatedpath loss information. The method can additionally include, based on theestimated path loss information, classifying, by the device, the cell asa type from a group of types of cells, the group including a macro celland a small cell.

In another aspect, a system as described herein can include a processorand a memory that stores executable instructions that, when executed bythe processor, facilitate performance of operations. The operations caninclude extracting signal strength information for a cell in a cellularcommunication network from log data associated with a user equipment,estimating path loss information associated with the cell at respectivedistances based on the signal strength information for the cell,resulting in estimated path loss information, and based on the estimatedpath loss information, classifying the cell as one from a group of typesof cells, the group including a macro cell and a small cell.

In a further aspect, a machine-readable storage medium as describedherein can include executable instructions that, when executed by aprocessor, facilitate performance of operations. The operations caninclude extracting signal strength information for a cell in a cellularcommunication network from log data logged in connection with a userequipment, estimating path loss information associated with the cell atrespective distances based on the signal strength information for thecell, resulting in estimated path loss information, and based on theestimated path loss information, classifying the cell as a type from agroup of types of cells, the group including a macro cell and a smallcell.

Referring first to FIG. 1, diagram 100 illustrates an example cellularcommunication environment in which various aspects described herein canfunction. As shown in diagram 100, the cellular communicationenvironment can include respective cells 10, which can each providecommunication coverage for a corresponding geographic area. In variousaspects, a cell 10 can be, or can include the functionality of, anaccess point, a base station, a Node B or an Evolved Node B (eNB),and/or any other suitable device(s). While the term “cell” can also beused to refer to the geographic area that is provided with communicationcoverage by such a device, it should be appreciated that the term “cell”as used herein refers only to the device that provides communicationcoverage for the area. For clarity of explanation, the area serviced bya given cell 10 is referred to as the coverage area for the cell 10.

In an aspect, cells 10 in a cellular network can vary in scale toprovide coverage for differing areas having different needs or sizes.For instance, a cell 10 can be a macro cell, which can providecommunication coverage for large urban or rural areas (e.g., at acoverage range of approximately 25 km or greater). Alternatively, a cell10 can be a small cell, such as a micro cell or a pico cell, which canprovide coverage for smaller areas than that associated with a macrocell. By way of non-limiting example, a micro cell can be utilized toprovide communication coverage for an area approximately the size of oneor more city blocks, and a pico cell can be used to providecommunication coverage for an area approximately the size of one or morebuildings. As used herein, the term “small cell” refers to both microcells and pico cells, as well as any other suitable cell type (e.g.,femto cells, etc.) that covers an area that is smaller than thatassociated with a macro cell.

In a cellular communication network such as the one depicted by diagram100, it can be desirable to collect and/or maintain data pertaining tothe network and its respective cells 10. For example, in the course ofdeploying new cells 10 to the network, changing configurations ofexisting cells 10, and/or otherwise managing the network, it can bedesirable to obtain knowledge relating to the existing networklandscape. This, in turn, can enable a network operator to better servecellular users, e.g., by improving communication quality and/or overalluser experience.

To the above and/or related ends, respective network logging devices 12can be used to collect information regarding the network and itsassociated cells 10. In general, a network logging device 12 can be anyuser equipment (UE) and/or other device(s) having the ability to observeand record information relating to the network and/or respective cells10 in the network. A network logging device 12 can be, for example, adrive test vehicle that is equipped with tools for analyzing andcollecting data on respective cells 10 in its surrounding area. By wayof example, information that can be collected by a network loggingdevice 12 can include, but are not limited to, information relating tothe frequency band(s) and/or bandwidth used by respective cells 10,transmit power utilized by respective cells 10, etc. Examples of theseand other types of information, as well as techniques for using saidinformation, are described in greater detail below.

Gathering information on a cellular communication network, such as by adrive test, can result in a very large amount of collected data. Forinstance, a single drive test can utilize a large number of networklogging devices 12 that can collectively gather information overhundreds of thousands of miles of travel, which can span hundreds ofcellular markets. Due to technical constrains as well as the limitednature of information available to individual network logging devices12, it is not feasible for extensive data analysis to be performed inreal-time during a drive test. As a result, a drive test can result in avast amount of unprocessed data for subsequent analysis andclassification. Due to the sheer scale of data collected by the networklogging devices 12 on a typical drive test, a human would be unable toprocess any meaningful portion of the collected data in a useful orreasonable timeframe.

Accordingly, various aspects herein can utilize a cell classificationsystem 110 that can receive data collected by respective network loggingdevices 12, e.g., via a drive test or other means, and utilize machinelearning and/or other suitable techniques to perform useful analysis onthe collected data. For instance, the cell classification system 110 canclassify respective cells 10 in the network as macro cells or smallcells based on the collected data. By way of specific, non-limitingexample, information that can be utilized by the cell classificationsystem 110 can include cell location, cell transmit power, path lossand/or distance to a given cell at respective locations, etc. Respectivetypes of information that can be utilized by the cell classificationsystem 110, as well as techniques for using such information, aredescribed in more detail below.

Turning to FIG. 2, a block diagram of a system 200 that facilitatessmall cell identification using machine learning in accordance withvarious aspects described herein is illustrated. As shown by FIG. 2, thesystem 200 can include one or more network logging devices 12, which cancollect information relating to cells of a cellular communicationnetwork (e.g., cells 10 as shown in diagram 100) and provide thatinformation to a cell classification system 110. In an aspect, thenetwork logging devices 12 can provide information to the cellclassification system 110 in real time or near-real time, e.g., as thatinformation is collected during the course of a drive test or otherlogging operation. Also or alternatively, the network logging devices 12can provide collected information to the cell classification system 110at the conclusion of logging.

As further illustrated by FIG. 2, the cell classification system 110 caninclude at least one processor 210 and a memory 212. In an aspect, theprocessor(s) 210 and memory 212 of the cell classification system 110can be associated with a single computing device or distributed acrossmultiple computing devices. For instance, in some embodiments the cellclassification system 110 can include a cluster and/or other grouping ofcomputing devices that each include one or more individual processors210. In this manner, respective operations performed by the cellclassification system 110 can be distributed among the differentprocessors 210 and/or computing devices associated with the cellclassification system 110. It should be appreciated, however, that insome embodiments the cell classification system 110 may not be adistributed system, and that other configurations are also possible.

In an aspect, the memory 212 of the cell classification system 110 caninclude volatile and/or non-volatile memory, each of which can beutilized for various purposes as appropriate. For instance, the memory212 can store information received from the network logging device(s) 12in connection with drive tests and/or other network analysis procedures.Additionally, the memory 212 can store computer-executable instructionsthat, when executed by the processor(s) 210, can cause the processor(s)210 to execute one or more functions. Various examples of functions thatcan be performed by the processor(s) 210 of the cell classificationsystem 110 in response to instructions provided by the memory 212 areprovided below. For simplicity of explanation, these functions aredescribed below in the context of computer-executable components thatcan be implemented, at least in part, by the processor(s) 210 inresponse to the appropriate instructions from the memory 212. It shouldbe appreciated, however, that the various components described hereincould be implemented at least partially in hardware in addition tosoftware, e.g., via a processor 210 executing instructions stored by thememory 212.

With reference now to FIG. 3, a block diagram of an example system 300for cell classification is illustrated. As shown by FIG. 3, system 300can include a data extraction component 310, a path loss estimationcomponent 320, and a classification component 330, which can be utilizedby the cell classification system 110 and/or other suitable systems forclassifying a cell given by network logging data as a macro cell or asmall cell (e.g., a micro cell or a pico cell).

In an aspect, the data extraction component 310 can extract signalstrength information and/or other information for a cell (e.g., a cell10) in a cellular communication network from UE log data, e.g., datareceived from one or more network logging devices 12. Informationobtained by the data extraction component 310 can include, but is notlimited to, the geographic location of the cell (e.g., given by latitudeand longitude, etc.), the geographic location of the correspondingnetwork logging device 12 at the time of recording the information,UE-level reference signal parameters such as reference signal receivedpower (RSRP) and/or reference signal received quality (RSRQ) for thecell and/or its neighboring cells, frequency information for the celland/or its neighboring cells, or the like. In an aspect, frequencyinformation for one or more network cells can be given in terms ofchannel information, e.g., as an evolved absolute radio frequencychannel number (EARFCN) or similar indicators, which can be converted bythe data extraction component 310 into a corresponding frequency orfrequency band. Other cell-level information can also be collected by anetwork logging device 12 and/or extracted by the data extractioncomponent 310.

Given the data for a cell as extracted by the data extraction component310, the path loss estimation component 320 can estimate path lossinformation associated with the cell, e.g., at respective distances.Additionally, based on various types of information extracted by thedata extraction component 310 as described above, other data can also beextracted including, but not limited to, the distance between the celland a UE (e.g., a network logging device 12) associated withmeasurements of that cell, path loss to the serving cell, geometry ofthe serving cell, etc. In an aspect, the path loss estimation component320 can derive other properties of a given network cell based oninformation received from the data extraction component 310 and/or othersources. For instance, based on reported data given by the dataextraction component 310 as well as one or more cell databases, the pathloss estimation component 320 can determine the transmit power used by agiven network cell. Other types of information can also be derived.

Based on path loss information as estimated by the path loss estimationcomponent 320, the classification component 330 can classify a givennetwork cell as, e.g., a macro cell or a small cell. In an aspect, theclassification component 330 can utilize data obtained by the dataextraction component 310 and/or one or more other sources in additionto, or in place of, the estimated path loss data given by the path lossestimation component 320. For example, the classification component 330can classify a network cell based at least in part on transmit powerdata for a network cell as extracted by the data extraction component310 from UE log data, e.g., since in some cases a small cell can utilizelower transmit power levels than that of a macro cell. In anotherexample, the classification component 330 can estimate a geometry of anetwork cell based on UE log data extracted by the data extractioncomponent 310 and classify the network cell based at least in part onthe estimated cell geometry. Other properties that can be utilized bythe classification component 330 to distinguish a small cell from amacro cell can include, but are not limited to, cell antenna height,RSRP ranges associated with the cell, a downlinksignal-plus-interference to noise ratio (SINR) for the cell, a transmitpower range assigned to one or more UEs by the cell, a physical resourceblock (PRB) allocation utilized by the cell (e.g., a larger allocationof PRBs to a given UE can indicate a small cell due to a small celltypically serving a smaller number of UEs than a macro cell), and/orother types of information.

In an aspect, the classification component 330 can leverage one or moreproperties of various cell types, such as macro cells, micro cells, andpico cells, to classify a given cell as one of said cell types. Forinstance, system 300 can define transmit power ranges that are generallyassociated with macro cells and small cells, respectively, and thesepower ranges can be used by the classification component 330 in itscomputations. By way of specific, non-limiting example, system 300 canidentify a first transmit power range, e.g., in decibel-milliwatts ordBm, for small cells (e.g., approximately 30 dBm to approximately 40dBm) and a second transmit power range for macro cells (e.g.,approximately 40 dBm to approximately 50 dBm). Based on the transmitpower of a given cell as determined by the path loss estimationcomponent 320 and/or the classification component 330, theclassification component 330 can then utilize the transmit power rangesin its classification. For instance, with reference to the exampleranges given above, the classification component could consider a cellwith a transmit power of less than approximately 37 dBm as more likelyto be a small cell while considering a cell with a transmit power ofapproximately 40 dBm or more as more likely to be a macro cell. In anaspect, this analysis can be combined with analysis based on otherfactors, such as path loss over distance, to arrive at a finalclassification for a given cell.

As another example, since a macro cell typically has a greatercommunication range than a small cell, the classification component 330can utilize distance information in UE log data extracted by the dataextraction component 310 in determining whether a given cell is a macrocell or a small cell. For instance, if a sample set obtained from UE logdata for a given cell contains a high percentage of samples from a largedistance from the cell (e.g., greater than approximately 400 m), theclassification component 330 can consider the cell as more likely to bea macro cell. Conversely, if a relatively large percentage of samplesfor the cell are from a short distance from the cell (e.g., less thanapproximately 200 m or less than approximately 400 m), theclassification component 330 can consider the cell as more likely to bea small cell.

As a further example, the classification component 330 can utilize pathloss data generated by the path loss estimation component 320 toestimate path loss and/or received signal power at respective distancesfrom a given cell in order to calculate a rate of increase of path lossversus distance for the cell. In one example, this rate of increase canbe substantially logarithmic over all or part of the range of distancesassociated with the cell.

In an aspect, due to the generally larger range associated with macrocells, path loss can increase with distance more slowly in a macro cellthan a similar small cell. By way of illustrative example, diagram 400in FIG. 4 shows an example logarithmic regression that can be performedbased on path loss samples at various distances. Here, two trend lines410, 412 are used, which correspond to different cells in a cellularnetwork. For clarity of illustration, diagram 400 has been simplifiedand does not show the individual samples used in computing the trendlines 410, 412. As shown by diagram 400, path loss increases withdistance faster for the cell represented by trend line 410 than that forthe cell illustrated by trend line 412. Accordingly, the classificationcomponent 330 can consider the cell corresponding to trend line 410 asmore likely to be a small cell while considering the cell correspondingto trend line 412 as more likely to be a macro cell.

Similarly, the generally larger range associated with macro cells cancause the RSRP associated with a cell to decrease with distance moreslowly in a macro cell than in a small cell. By way of anotherillustrative example, diagram 500 in FIG. 5 shows an example logarithmicregression that can be performed based on RSRP samples at variousdistances. Here, two trend lines 510, 512 are used, which correspond todifferent cells in a cellular network. For clarity of illustration,diagram 500 has been simplified and does not show the individual samplesused in computing the trend lines 510, 512. As shown by diagram 500,RSRP decreases with distance faster for the cell represented by trendline 510 than that for the cell illustrated by trend line 512.Accordingly, the classification component 330 can consider the cellcorresponding to trend line 510 as more likely to be a small cell whileconsidering the cell corresponding to trend line 512 as more likely tobe a macro cell.

Returning to FIG. 3, the data extraction component 310 can obtain raw UElog data, e.g., from one or more network logging devices 12 as collectedvia a drive test or other means, and transform the UE log data into celllevel data and/or other suitable data types. By way of example, a drivetest log can be constructed in a tabular format having rowscorresponding to UE information obtained at a given timestamp.Respective rows of the drive test table can include various fields suchas the following:

1) Timestamp

2) UE location, e.g., given as latitude/longitude

3) Frequency band (e.g., given as an EARFCN) used for measurement

4) Physical cell ID of the serving cell at the time of measurement

5) Signal quality associated with a serving cell at the time ofmeasurement, e.g., given as RSRP and/or RSRQ

6) Cell ID of the serving cell at the time of measurement

Other fields can also be utilized.

In an aspect, the data extraction component 310 can consult a celldatabase and/or other information sources to obtain additionalinformation to supplement the UE log data. For instance, given aphysical cell ID, cell ID, and/or EARFCN associated with a cell asreported in the UE log data, the approximate transmit power, location(e.g., given as latitude/longitude), antenna height, and/or otherproperties of the cell can be found via the cell database. Subsequently,the distance between the UE and its serving cell for each measurementcan be computed based on the UE location data given by the UE log dataas well as the cell location data given by the cell database.Additionally, the UE log data for a given UE location can be used incombination with domain knowledge to derive the path loss associatedwith that UE location. In an aspect, this can be calculated with respectto energy per resource element (EPRE) as follows:

path loss=EPRE−RSRP=TX power−log₁₀(bandwidth×5×12)−RSRP.

As a result of the above and/or other operations, the data extractioncomponent 310 can produce a set of tabular cell-level data. In anaspect, the cell-level data can include respective rows, and these rowscan in turn include information such as UE location (e.g., given aslatitude/longitude), RSRP (e.g., given in dBm), UE distance to itsserving cell, path loss, residing cell transmit power (e.g., given indBm), and/or other suitable information.

As noted above, the amount of UE log data that can be generated during adrive test and/or other similar operations can be significantly large,e.g., of a scale that cannot be analyzed by a human in a useful orreasonable timeframe. As a result, the classification component 330 canemploy one or more machine learning algorithms to receive input UE logdata and classify respective cells associated with the UE log data in anautomated manner. This, in turn, allows network trends and/or otheruseful information to be derived from the UE log data, which can beutilized to improve network performance and/or provide other advantagesor improvements to the operation of a cellular communication networkthat would be difficult or unfeasible to realize without use of theclassification component 330 as described herein. Two examples ofmachine learning techniques that can be utilized by the classificationcomponent 330, namely a support vector machine (SVM) and a neuralnetwork, are provided herein. It should be appreciated, however, thatthese are merely examples of machine learning techniques that could beused and that other techniques are also possible. For instance, amachine learning algorithm based on a decision tree, a random forest, orthe like could be used. Other algorithms and/or techniques are alsopossible.

Turning now to FIG. 6, a block diagram of a system 600 that facilitatessmall cell identification using an SVM in accordance with variousaspects described herein is illustrated. Repetitive description of likeelements employed in other embodiments described herein is omitted forsake of brevity. As shown by FIG. 6, system 600 includes aclassification component 330 that can receive path loss data from a pathloss estimation component 320 as described above. Also or alternatively,the classification component 330 can receive further information fromother sources, such as the data extraction component 310 shown in FIG.3.

In an aspect, the classification component 330 can include an SVMcomponent 610 that can classify a cell given in associated log data asone of a group of types (e.g., as a macro cell or a small cell) usingone or more SVM techniques. An example technique that can be employed bythe SVM component 610 is provided below. It should be appreciated,however, that other techniques could also be used.

In an example, the SVM component 610 can generate a logarithmicregression to represent path loss vs. distance based on data provided bythe path loss estimation component 320 and/or other sources. In anaspect, a logarithmic regression can be generated by the SVM component610 to approximate the change of path loss with distance as alogarithmic curve, e.g., similar to that shown by the simplified examplein FIG. 4. In one example, the SVM component 610 can determine arepresentative curve for the path loss data given by the path lossestimation component 320 in the form y=θ₁+θ₂ log₁₀x, where θ₁ representsan initial path loss and θ₂ represents a rate of path loss change withdistance. The parameters θ₁ and θ₂ corresponding to the curve can beestimated by the SVM component 610 from the given path loss data. In anaspect, this estimation can be done in two stages. More particularly,the SVM component 610 can compute the logarithmic regression andassociated values for θ₁ and θ₂ in the first stage, and subsequentlypredict the path loss associated with a given cell at respectivedistances in the second stage.

In an aspect, the SVM component 610 can record respective logarithmicregressions and corresponding predictions in a table, such as table 700shown in FIG. 7, and/or another suitable data structure. Table 700 asshown in FIG. 7 can include rows containing information for respectivenetwork cells. Each row can contain information such as a cell index(which can be an index assigned by the network or a separate index usedfor purposes of table 700) and other information associated with thecell such as a cell transmit power and a cell frequency band. As furthershown by table 700, respective rows can contain the values for θ₁ and θ₂as computed by the SVM component 610, as well as predicted path loss(PL) values for respective distances based on θ₁ and θ₂. Here, values of100 m, 500 m, and 1000 m are used, but it should be appreciated thatother values could also be used. In an aspect, based on the predictedpath loss values generated by the SVM component 610 as well as otheravailable information for a given cell, the SVM component 610 canclassify the cell as a macro cell or a small cell, e.g., using one ormore considerations as described above.

In another aspect, the SVM component 610 can perform a similar analysisto that described above with respect to path loss to classify cell RSRP.Thus, for example, the SVM component 610 can generate values θ₁ and θ₂for a logarithmic regression corresponding to RSRP for a given cellversus distance, based on which the SVM component 610 can generateestimates for the RSRP at various distances in a similar manner to thatdescribed above with respect to path loss. In one example,classification of a given cell based on RSRP can be dependent on thetransmit power utilized by the cell since transmit power profiles candiffer between different cells and/or cell operators.

With reference next to FIG. 8, a block diagram of a system 800 thatfacilitates small cell identification using a neural network inaccordance with various aspects described herein is illustrated.Repetitive description of like elements employed in other embodimentsdescribed herein is omitted for sake of brevity. As shown by FIG. 8,system 800 includes a path loss estimation component 320 and aclassification component 330 that can interact in a similar manner tothat described above with respect to FIG. 6. In an aspect, theclassification component 330 can include a neural network component 810that can classify a cell given in associated log data as one of a groupof types (e.g., as a macro cell or a small cell) using one or moreneural network techniques. An example technique that can be employed bythe neural network component 810 is provided below. It should beappreciated, however, that other techniques could also be used Forinstance, a machine learning algorithm based on a decision tree, arandom forest, or the like could be used. Other algorithms and/ortechniques are also possible.

In an aspect, the neural network component 810 can start operation byaggregating UE-level to cell-level information as described above intobins corresponding to respective distance ranges. The bins employed bythe neural network component 810 can correspond to uniform distanceranges (e.g., 25-meter bins, 50-meter bins, etc.) or non-uniform ranges.In a specific, non-limiting example that employs 25-meter sized bins,the neural network component 810 can utilize average path loss overrespective 25-meter intervals to populate the bins. Thus, for instance,average UE path loss associated with distances to a given cell between0-25 meters can be assigned to a 25-meter bin, average UE path lossassociated with distances to the cell between 25-50 meters can beassigned to a 50-meter bin, and so on. In an aspect, assignment of UEdata to respective bins can be performed by, e.g., a distributedcomputing system operating via the Apache Hadoop software platform.Other techniques could also be used.

In an aspect, the neural network component 810 can record informationcorresponding to bins as described above in a table, such as table 900shown in FIG. 9, and/or another suitable data structure. Table 900 asshown in FIG. 9 can include rows containing information for respectivenetwork cells. Each row can contain a cell index, transmit power, andfrequency band in a similar manner to table 700 in FIG. 7. As furthershown by table 900, respective rows can contain average path loss forrespective bins, here 50-meter bins. While table 900 shows only binscorresponding to distances up to 250 m for brevity, it should beappreciated that any number of bins can be utilized by the neuralnetwork component 810 for any suitable number of corresponding distanceranges. For instance, table 900 could include additional bins for50-meter intervals up to any suitable maximum distance (e.g., 1000 m,2500 m, etc.).

As shown by table 900, the neural network component 810 may be unable topopulate each distance bin for each network cell, e.g., due to a lack ofUE log data for a given cell at various ranges. Accordingly, values forwhich insufficient log data is present can initially be left blank intable 900. In order to provide a more complete estimate of cellperformance at all available ranges, path loss for various rangescorresponding to a cell can be interpolated based on existing data forthe cell at other ranges. With reference to FIG. 10, a system 1000 thatassists the neural network component 810 by interpolating cell path lossdata is illustrated. System 1000 includes an interpolation component1010 that can be employed by the classification component 330 tointerpolate missing data generated by the neural network component 810,e.g., corresponding to table 900. In an aspect, the interpolationcomponent 1010 can utilize path loss information for a first distance(e.g., a first distance bin as shown by table 900) to interpolate otherpath loss information for a second distance (e.g., a second distance binas shown by table 900) that is different than the first distance. As aresult of performing interpolation on path loss data via theinterpolation component 1010, respective missing data points in the pathloss data can be estimated to facilitate more robust classification.

In an aspect, the interpolation component 1010 can utilize logarithmicinterpolation to estimate missing bin values associated with the neuralnetwork component 810. An example technique that can be utilized by theinterpolation component 1010 for logarithmic interpolation is describedbelow. It should be appreciated, however, that other techniques forlogarithmic interpolation, as well as different types of interpolation,could also be used.

In an aspect, logarithmic interpolation as performed by theinterpolation component 1010 can be utilized to estimate missing pathloss values (e.g., blank values as shown in table 900) between a set ofknown data points, e.g., two points D₁ and D₂ (e.g., corresponding totwo different distance bins as described above) and their respectivepath loss values p₁ and p₂. By way of a non-limiting example that uses25-meter bins, point D₁ can correspond to an x-meter bin and point D₂can correspond to an (x+25y)-meter bin, where y is the number of missingdata points between D₁ and D₂. In the event that no known point D₁ or D₂exists, e.g., the missing data point is associated with a first bin or alast bin, an estimated initial or final path loss can be calculated,e.g., using logarithmic regression as described above and/or by othermeans.

Based on data points D₁ and D₂, the logarithmic slope for the segmentbetween D₁ and D₂ can be found as follows:

Slope=(p ₂ −p ₁)/(log₁₀ d ₂−log₁₀ d ₁).

where d₁ and d₂ are the distances (e.g., bin distances) associated withpoints D₁ and D₂, respectively.

Next, the path loss p_(m) for a given data point D_(m) between D₁ and D₂can be given by the following:

p _(m) =p ₁+Slope×(log₁₀ d _(m)−log₁₀ d ₁)

where d_(m) is the distance (e.g., bin distance) associated with pointD_(m). An example of logarithmic interpolation that can be performed inthe above manner for a set of data points is shown by diagram 1100 inFIG. 11.

In an aspect, the above logarithmic interpolation technique can beutilized by the interpolation component 1010 since path loss in acellular communication network can exhibit path loss as a per-decade(e.g., based on log₁₀) property. Additionally, use of the neural networkcomponent 810 and the interpolation component 1010 as described hereincan result in increased accuracy of path loss estimation by accountingfor non-uniform rates of path loss change with distance, since the rateat which path loss changes with distance for a given cell may not beuniform for all distance ranges.

While the above description relates to the specific, non-limitingexample of logarithmic interpolation, it should be appreciated thatother types of interpolation could also be used by the interpolationcomponent 1010. For instance, the interpolation could use linearinterpolation, quadratic interpolation, exponential interpolation,and/or any other suitable technique(s).

Returning again to FIG. 3, regardless of the machine learning approachemployed by the classification component 330, the classificationcomponent 330 can utilize a training set to train the machine learningalgorithm(s) used as well as any respectively corresponding models. Inan aspect, a training set utilized by the classification component 330can include drive test data and/or other UE log data that corresponds torespective cells with known classifications. However, because there aregenerally more macro cells deployed in cellular communication networksthan small cells, and because macro cells have a greater range thansmall cells, a training set corresponding to UE log data can in somecases be significantly skewed toward samples for macro cells. This, inturn, can impact the prediction accuracy of the classification component330. In order to balance samples of a training set obtained via UE logdata between macro cells and small cells, oversampling can be performedon the cell-level data that is generated as described above by creatingduplicate or “artificial” small cells. Cell duplication in this mannercan be performed such that the ratio of macro cells to small cells inthe training set is approximately any desired ratio (e.g., 1:1, 2:1,etc.). In order to increase the generality of the duplicated small celldata, modifications can be made to the cell-level data for the duplicatecells. In an aspect, modifications made in this manner can besufficiently minor such that the path loss signature of the cells ismaintained while still improving the robustness of the training set.

By way of specific, non-limiting example, a set of cell-level trainingdata can include 40 bins that encompass respective 25-meter bins between0 m and 1000 m. To improve the generality of the training data, for each2.5% of the training samples, a path loss corresponding to one of the 40bins can be increased or decreased by a small amount (e.g., 0.1 dB),thereby modifying each of the bins in the training set an approximatelyequal amount of times. In an aspect, modification in this manner to asingle bin enables the training data to be more generalized and abundantwithout changing the cell types represented by the path loss. Withrespect to the above example, it should be appreciated that any numberof samples and/or bins could also be used, provided that the bins in thetraining data are modified substantially equally.

In another aspect, the relationship between path loss and distance canvary for different frequencies. As a result, the classificationcomponent 330 can further utilize frequency information to improveperformance of the machine learning technique(s) employed by theclassification component 330. In one example, the classification canutilize a mapping between EARFCN and frequency to obtain frequency datafor cells having a known EARFCN. Table 1 below illustrates an exampleEARFCN to frequency mapping that can be utilized for respective cells ina cellular communication network. It should be appreciated thatrespective frequencies derived from EARFCN data need not precisely matchwith the center frequency of the corresponding cell(s) provided that thefrequency mapping can enable the classification component 330 todistinguish between low, mid, and high frequency bands.

TABLE 1 EARFCN to frequency mapping for an example cellular network.EARFCN Frequency (MHz) 650 1900 850 1900 975 1900 1150 1900 1975 21002000 2100 2050 2100 2175 2100 5230 700 5780 700 9820 2300

As noted above, the classification component 330 can utilize path lossand/or signal strength data in combination with other types of networkdata to improve cell classification performance By way of example, thedata extraction component 310 shown in FIG. 1 can extract transmit powerdata for a cell based on UE log data and/or other information, and theclassification component 330 can classify the cell (e.g., as a macrocell or a small cell) based at least in part on the transmit power data.As another example, the data extraction component 310 can estimate ageometry of a cell based on UE log data and/or other information for thecell, and the classification component 330 can classify the cell basedat least in part on the estimated cell geometry. As a further example,the data extraction component 310 can extract or otherwise determinedistance data from the UE log data, such as a maximum distance from thecell as given by a sample in the UE log data and/or a percentage ofsamples given in the UE log data that indicate distances within athreshold distance from the cell (e.g., within 300 m, within 500 m,etc.), and the classification component 330 can classify the cell basedat least in part on the distance data. As still another example, thedata extraction component 310 can identify an antenna transmissionfrequency associated with the cell from the UE log data, and theclassification component 330 can classify the cell based at least inpart on the antenna transmission frequency. Other forms of supplementalinformation, including those described above and/or other types ofsupplemental information, are also possible.

FIG. 12 illustrates a method in accordance with certain aspects of thisdisclosure. While, for purposes of simplicity of explanation, the methodis shown and described as a series of acts, it is to be understood andappreciated that this disclosure is not limited by the order of acts, assome acts may occur in different orders and/or concurrently with otheracts from that shown and described herein. For example, those skilled inthe art will understand and appreciate that methods can alternatively berepresented as a series of interrelated states or events, such as in astate diagram. Moreover, not all illustrated acts may be required toimplement methods in accordance with certain aspects of this disclosure.

With reference to FIG. 12, a flow diagram of a method 1200 for smallcell classification using machine learning is presented. At 1202, adevice comprising a processor (e.g., a device of a cell classificationsystem 110 comprising a processor 210) can extract (e.g., by a dataextraction component 310) signal strength information for a cell (e.g.,a cell 10) in a cellular communication network from UE log data (e.g.,data collected by one or more network logging devices 12).

At 1204, the device can estimate (e.g., by a path loss estimationcomponent 320) path loss information associated with the cell atrespective distances based on the signal strength information for thecell as extracted at 1202, resulting in estimated path loss information.

At 1206, the device can classify (e.g., by a classification component330) the cell, e.g., as a macro cell or a small cell, based on theestimated path loss information obtained at 1204.

In order to provide additional context for various embodiments describedherein, FIG. 13 and the following discussion are intended to provide abrief, general description of a suitable computing environment 1300 inwhich the various embodiments of the embodiment described herein can beimplemented. While the embodiments have been described above in thegeneral context of computer-executable instructions that can run on oneor more computers, those skilled in the art will recognize that theembodiments can be also implemented in combination with other programmodules and/or as a combination of hardware and software.

Generally, program modules include routines, programs, components, datastructures, etc., that perform particular tasks or implement particularabstract data types. Moreover, those skilled in the art will appreciatethat the inventive methods can be practiced with other computer systemconfigurations, including single-processor or multiprocessor computersystems, minicomputers, mainframe computers, as well as personalcomputers, hand-held computing devices, microprocessor-based orprogrammable consumer electronics, and the like, each of which can beoperatively coupled to one or more associated devices.

The illustrated embodiments of the embodiments herein can be alsopracticed in distributed computing environments where certain tasks areperformed by remote processing devices that are linked through acommunications network. In a distributed computing environment, programmodules can be located in both local and remote memory storage devices.

Computing devices typically include a variety of media, which caninclude computer-readable storage media and/or communications media,which two terms are used herein differently from one another as follows.Computer-readable storage media can be any available storage media thatcan be accessed by the computer and includes both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable storage media can be implementedin connection with any method or technology for storage of informationsuch as computer-readable instructions, program modules, structured dataor unstructured data.

Computer-readable storage media can include, but are not limited to,random access memory (RAM), read only memory (ROM), electricallyerasable programmable read only memory (EEPROM), flash memory or othermemory technology, compact disk read only memory (CD-ROM), digitalversatile disk (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devicesor other tangible and/or non-transitory media which can be used to storedesired information. In this regard, the terms “tangible” or“non-transitory” herein as applied to storage, memory orcomputer-readable media, are to be understood to exclude onlypropagating transitory signals per se as modifiers and do not relinquishrights to all standard storage, memory or computer-readable media thatare not only propagating transitory signals per se.

Computer-readable storage media can be accessed by one or more local orremote computing devices, e.g., via access requests, queries or otherdata retrieval protocols, for a variety of operations with respect tothe information stored by the medium.

Communications media typically embody computer-readable instructions,data structures, program modules or other structured or unstructureddata in a data signal such as a modulated data signal, e.g., a carrierwave or other transport mechanism, and includes any information deliveryor transport media. The term “modulated data signal” or signals refersto a signal that has one or more of its characteristics set or changedin such a manner as to encode information in one or more signals. By wayof example, and not limitation, communication media include wired media,such as a wired network or direct-wired connection, and wireless mediasuch as acoustic, RF, infrared and other wireless media.

With reference again to FIG. 13, the example environment 1300 forimplementing various embodiments of the aspects described hereinincludes a computer 1302, the computer 1302 including a processing unit1304, a system memory 1306 and a system bus 1308. The system bus 1308couples system components including, but not limited to, the systemmemory 1306 to the processing unit 1304. The processing unit 1304 can beany of various commercially available processors. Dual microprocessorsand other multi-processor architectures can also be employed as theprocessing unit 1304.

The system bus 1308 can be any of several types of bus structure thatcan further interconnect to a memory bus (with or without a memorycontroller), a peripheral bus, and a local bus using any of a variety ofcommercially available bus architectures. The system memory 1306includes ROM 1310 and RAM 1312. A basic input/output system (BIOS) canbe stored in a non-volatile memory such as ROM, erasable programmableread only memory (EPROM), EEPROM, which BIOS contains the basic routinesthat help to transfer information between elements within the computer1302, such as during startup. The RAM 1312 can also include a high-speedRAM such as static RAM for caching data.

The computer 1302 further includes an internal hard disk drive (HDD)1314 (e.g., EIDE, SATA), a magnetic floppy disk drive (FDD) 1316, (e.g.,to read from or write to a removable diskette 1318) and an optical diskdrive 1320, (e.g., reading a CD-ROM disk 1322 or, to read from or writeto other high capacity optical media such as the DVD). While theinternal HDD 1314 is illustrated as located within the computer 1302,the internal HDD 1314 can also be configured for external use in asuitable chassis (not shown). The HDD 1314, magnetic FDD 1316 andoptical disk drive 1320 can be connected to the system bus 1308 by anHDD interface 1324, a magnetic disk drive interface 1326 and an opticaldrive interface 1328, respectively. The interface 1324 for externaldrive implementations includes at least one or both of Universal SerialBus (USB) and Institute of Electrical and Electronics Engineers (IEEE)1394 interface technologies. Other external drive connectiontechnologies are within contemplation of the embodiments describedherein.

The drives and their associated computer-readable storage media providenonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For the computer 1302, the drives andstorage media accommodate the storage of any data in a suitable digitalformat. Although the description of computer-readable storage mediaabove refers to an HDD, a removable magnetic diskette, and a removableoptical media such as a CD or DVD, it should be appreciated by thoseskilled in the art that other types of storage media which are readableby a computer, such as zip drives, magnetic cassettes, flash memorycards, cartridges, and the like, can also be used in the exampleoperating environment, and further, that any such storage media cancontain computer-executable instructions for performing the methodsdescribed herein.

A number of program modules can be stored in the drives and RAM 1312,including an operating system 1330, one or more application programs1332, other program modules 1334 and program data 1336. All or portionsof the operating system, applications, modules, and/or data can also becached in the RAM 1312. The systems and methods described herein can beimplemented utilizing various commercially available operating systemsor combinations of operating systems.

A user can enter commands and information into the computer 1302 throughone or more wired/wireless input devices, e.g., a keyboard 1338 and apointing device, such as a mouse 1340. Other input devices (not shown)can include a microphone, an infrared (IR) remote control, a joystick, agame pad, a stylus pen, touch screen or the like. These and other inputdevices are often connected to the processing unit 1304 through an inputdevice interface 1342 that can be coupled to the system bus 1308, butcan be connected by other interfaces, such as a parallel port, an IEEE1394 serial port, a game port, a USB port, an IR interface, etc.

A monitor 1344 or other type of display device can be also connected tothe system bus 1308 via an interface, such as a video adapter 1346. Inaddition to the monitor 1344, a computer typically includes otherperipheral output devices (not shown), such as speakers, printers, etc.

The computer 1302 can operate in a networked environment using logicalconnections via wired and/or wireless communications to one or moreremote computers, such as a remote computer(s) 1348. The remotecomputer(s) 1348 can be a workstation, a server computer, a router, apersonal computer, portable computer, microprocessor-based entertainmentappliance, a peer device or other common network node, and typicallyincludes many or all of the elements described relative to the computer1302, although, for purposes of brevity, only a memory/storage device1350 is illustrated. The logical connections depicted includewired/wireless connectivity to a local area network (LAN) 1352 and/orlarger networks, e.g., a wide area network (WAN) 1354. Such LAN and WANnetworking environments are commonplace in offices and companies, andfacilitate enterprise-wide computer networks, such as intranets, all ofwhich can connect to a global communications network, e.g., theInternet.

When used in a LAN networking environment, the computer 1302 can beconnected to the local network 1352 through a wired and/or wirelesscommunication network interface or adapter 1356. The adapter 1356 canfacilitate wired or wireless communication to the LAN 1352, which canalso include a wireless access point (AP) disposed thereon forcommunicating with the wireless adapter 1356.

When used in a WAN networking environment, the computer 1302 can includea modem 1358 or can be connected to a communications server on the WAN1354 or has other means for establishing communications over the WAN1354, such as by way of the Internet. The modem 1358, which can beinternal or external and a wired or wireless device, can be connected tothe system bus 1308 via the input device interface 1342. In a networkedenvironment, program modules depicted relative to the computer 1302 orportions thereof, can be stored in the remote memory/storage device1350. It will be appreciated that the network connections shown areexample and other means of establishing a communications link betweenthe computers can be used.

The computer 1302 can be operable to communicate with any wirelessdevices or entities operatively disposed in wireless communication,e.g., a printer, scanner, desktop and/or portable computer, portabledata assistant, communications satellite, any piece of equipment orlocation associated with a wirelessly detectable tag (e.g., a kiosk,news stand, restroom), and telephone. This can include Wireless Fidelity(Wi-Fi) and BLUETOOTH® wireless technologies. Thus, the communicationcan be a predefined structure as with a conventional network or simplyan ad hoc communication between at least two devices.

Wi-Fi can allow connection to the Internet from a couch at home, a bedin a hotel room or a conference room at work, without wires. Wi-Fi is awireless technology similar to that used in a cell phone that enablessuch devices, e.g., computers, to send and receive data indoors and out;anywhere within the range of a base station. Wi-Fi networks use radiotechnologies called IEEE 802.11 (a, b, g, n, ac, etc.) to providesecure, reliable, fast wireless connectivity. A Wi-Fi network can beused to connect computers to each other, to the Internet, and to wirednetworks (which can use IEEE 802.3 or Ethernet). Wi-Fi networks operatein the unlicensed 2.4 and 5 GHz radio bands, at an 11 Mbps (802.11a) or54 Mbps (802.11b) data rate, for example or with products that containboth bands (dual band), so the networks can provide real-worldperformance similar to the basic 10BaseT wired Ethernet networks used inmany offices.

The above description includes non-limiting examples of the variousembodiments. It is, of course, not possible to describe everyconceivable combination of components or methodologies for purposes ofdescribing the disclosed subject matter, and one skilled in the art mayrecognize that further combinations and permutations of the variousembodiments are possible. The disclosed subject matter is intended toembrace all such alterations, modifications, and variations that fallwithin the spirit and scope of the appended claims.

With regard to the various functions performed by the above describedcomponents, devices, circuits, systems, etc., the terms (including areference to a “means”) used to describe such components are intended toalso include, unless otherwise indicated, any structure(s) whichperforms the specified function of the described component (e.g., afunctional equivalent), even if not structurally equivalent to thedisclosed structure. In addition, while a particular feature of thedisclosed subject matter may have been disclosed with respect to onlyone of several implementations, such feature may be combined with one ormore other features of the other implementations as may be desired andadvantageous for any given or particular application.

The terms “exemplary” and/or “demonstrative” as used herein are intendedto mean serving as an example, instance, or illustration. For theavoidance of doubt, the subject matter disclosed herein is not limitedby such examples. In addition, any aspect or design described herein as“exemplary” and/or “demonstrative” is not necessarily to be construed aspreferred or advantageous over other aspects or designs, nor is it meantto preclude equivalent structures and techniques known to one skilled inthe art. Furthermore, to the extent that the terms “includes,” “has,”“contains,” and other similar words are used in either the detaileddescription or the claims, such terms are intended to be inclusive—in amanner similar to the term “comprising” as an open transitionword—without precluding any additional or other elements.

The term “or” as used herein is intended to mean an inclusive “or”rather than an exclusive “or.” For example, the phrase “A or B” isintended to include instances of A, B, and both A and B. Additionally,the articles “a” and “an” as used in this application and the appendedclaims should generally be construed to mean “one or more” unless eitherotherwise specified or clear from the context to be directed to asingular form.

The term “set” as employed herein excludes the empty set, i.e., the setwith no elements therein. Thus, a “set” in the subject disclosureincludes one or more elements or entities. Likewise, the term “group” asutilized herein refers to a collection of one or more entities.

The terms “first,” “second,” “third,” and so forth, as used in theclaims, unless otherwise clear by context, is for clarity only anddoesn't otherwise indicate or imply any order in time. For instance, “afirst determination,” “a second determination,” and “a thirddetermination,” does not indicate or imply that the first determinationis to be made before the second determination, or vice versa, etc.

The description of illustrated embodiments of the subject disclosure asprovided herein, including what is described in the Abstract, is notintended to be exhaustive or to limit the disclosed embodiments to theprecise forms disclosed. While specific embodiments and examples aredescribed herein for illustrative purposes, various modifications arepossible that are considered within the scope of such embodiments andexamples, as one skilled in the art can recognize. In this regard, whilethe subject matter has been described herein in connection with variousembodiments and corresponding drawings, where applicable, it is to beunderstood that other similar embodiments can be used or modificationsand additions can be made to the described embodiments for performingthe same, similar, alternative, or substitute function of the disclosedsubject matter without deviating therefrom. Therefore, the disclosedsubject matter should not be limited to any single embodiment describedherein, but rather should be construed in breadth and scope inaccordance with the appended claims below.

What is claimed is:
 1. A method, comprising: extracting, by a devicecomprising a processor, signal strength information for a cell in acellular communication network from user equipment log data associatedwith a user equipment; estimating, by the device, path loss informationassociated with the cell at respective distances based on the signalstrength information for the cell, resulting in estimated path lossinformation; and based on the estimated path loss information,classifying, by the device, the cell as a type from a group of types ofcells, the group comprising a macro cell and a small cell.
 2. The methodof claim 1, wherein the classifying comprises classifying the cell asthe type from the group using a support vector machine.
 3. The method ofclaim 1, wherein the classifying comprises classifying the cell as thetype from the group using a neural network.
 4. The method of claim 3,wherein the estimated path loss information is first estimated path lossinformation, and wherein the estimating comprises interpolating thefirst estimated path loss information for a first distance of therespective distances based on second estimated path loss information fora second distance of the respective distances that is different from thefirst distance.
 5. The method of claim 4, wherein the estimating furthercomprises interpolating the first estimated path loss information vialogarithmic interpolation.
 6. The method of claim 1, wherein theextracting comprises extracting transmit power data for the cell fromthe user equipment log data, and wherein the classifying the cell as thetype from the group comprises classifying the cell further based on thetransmit power data.
 7. The method of claim 1, further comprising:estimating, by the device, a geometry of the cell based on the userequipment log data, resulting in an estimated cell geometry for thecell, wherein the classifying the cell as the type from the groupcomprises classifying the cell further based on the estimated cellgeometry.
 8. The method of claim 1, further comprising: determining, bythe device, a maximum distance from the cell as given by a sample in theuser equipment log data, wherein the classifying the cell as the typefrom the group comprises classifying the cell further based on themaximum distance.
 9. The method of claim 1, further comprising:determining, by the device, a percentage of samples given in the userequipment log data that indicate distances within a threshold distancefrom the cell, wherein the classifying the cell as the type from thegroup comprises classifying the cell further based on the percentage ofsamples.
 10. The method of claim 1, further comprising: identifying, bythe device, an antenna transmission frequency associated with the cellfrom the user equipment log data, wherein the classifying the cell asthe type from the group comprises classifying the cell further based onthe antenna transmission frequency.
 11. A system, comprising: aprocessor; and a memory that stores executable instructions that, whenexecuted by the processor, facilitate performance of operations, theoperations comprising: extracting signal strength information for a cellin a cellular communication network from log data associated with a userequipment; estimating path loss information associated with the cell atrespective distances based on the signal strength information for thecell, resulting in estimated path loss information; and based on theestimated path loss information, classifying the cell as one from agroup of types of cells, the group comprising a macro cell and a smallcell.
 12. The system of claim 11, wherein the classifying the cell asone from the group comprises: classifying the cell as one from the groupusing a neural network.
 13. The system of claim 12, wherein theestimated path loss information is first estimated path lossinformation, and wherein the operations further comprise: interpolatingthe first estimated path loss information for a first distance of therespective distances based on second estimated path loss information fora second distance of the respective distances that is different from thefirst distance.
 14. The system of claim 11, wherein the operationsfurther comprise: extracting transmit power data for the cell from thelog data, and wherein the classifying the cell as one from the groupcomprises classifying the cell as one from the group based on thetransmit power data.
 15. The system of claim 11, wherein the operationsfurther comprise: estimating a geometry of the cell based on the logdata, resulting in an estimated cell geometry for the cell, and whereinthe classifying the cell as one from the group comprises classifying thecell as one from the group based on the estimated cell geometry.
 16. Thesystem of claim 11, wherein the operations further comprise: extractingdistance data associated with distances between the user equipment andthe cell from the log data, and wherein the classifying the cell as onefrom the group comprises classifying the cell as one from the groupbased on the distance data.
 17. The system of claim 11, wherein theoperations further comprise: identifying an antenna transmissionfrequency associated with the cell from the log data, and wherein theclassifying the cell as one from the group comprises classifying thecell as one from the group based on the antenna transmission frequency.18. A machine-readable storage medium, comprising executableinstructions that, when executed by a processor, facilitate performanceof operations, comprising: extracting signal strength information for acell in a cellular communication network from log data logged inconnection with a user equipment; estimating path loss informationassociated with the cell at different distances based on the signalstrength information for the cell, resulting in estimated path lossinformation; and based on the estimated path loss information,classifying the cell as a type from a group of types of cells, the groupcomprising a macro cell and a small cell.
 19. The machine-readablestorage medium of claim 18, wherein the classifying the cell as the typefrom the group comprises: classifying the cell as the type from thegroup using a neural network.
 20. The machine-readable storage medium ofclaim 19, wherein the estimated path loss information is first estimatedpath loss information, and wherein the operations further comprise:interpolating the first estimated path loss information for a firstdistance of the different distances based on second estimated path lossinformation for a second distance of the different distances that isdifferent from the first distance.